Top 10 Data Engineer Interview Questions

Once you receive an interview request for a data engineering job, you need to have a solid understanding of the various processes in the domain.

12:10 12 August 2020

In recent times, a data engineer course is one of the most sought-after programs in the software development industry. Thanks to the growing popularity of data engineers among business owners to leverage the sheer possibilities from data analysis. Deemed unnecessary to businesses before, now brands generate massive amounts of data every minute that can help them to scale, build, and deliver better solutions to their customers with the help of the best data engineers in the field.

 

However, there are a few prerequisites for being a data engineer like:

 

• In-depth understanding of data modeling, in case of data warehousing and Big Data
  
  
• Experience in ETLs, Hadoop Stack, Hive, Pig, and others
  
  
• Strong knowledge in SQL, Python, and mathematics
  
  
• Enhanced data visualization skills via PowerBI or Tableau 
  
  

Once you receive an interview request for a data engineering job, you need to have a solid understanding of the various processes in the domain, especially given the rising competition within brands. Therefore, we have compiled some of the most common, relevant interview questions that can help you prepare for your interview and exceed expectations of your potential employer. Have a look!

Q1. What is data engineering, according to you?

Data engineering is an act of changing, cleaning, profiling, and segregating massive datasets to make them ready for further data query building processes and extraction. Since the data comes from multiple sources, it is essential to take extra precision to regulate data pipelines. Besides, a data engineer makes the data usable, accessible, and actionable for employees to make informed decisions.

Q2. What made you choose a career in data engineering?

By asking this question, the interviewer is looking more than just your motivation in data engineering. They are also trying to understand how you identify yourself with data, and learn about your passion in the data industry:

I have always been fascinated with figures generated by mathematical statistics and how they're implemented in many ways to gain insights, in general. Being comfortable juggling numbers and doing mathematics, I realized I wanted to do more in the field of data analysis while I was in my first job. I delved deeper into honing my programming and data management skills to understand how brands compete with each other in the market based on the data they generate every day. 

Q3. Tell us about the Big V's of Big Data.

1. There are four central V's in Big Data:
   
   

• Velocity - It talks about the rate of Big Data generated in due course of time.
  
  

• Variety - It refers to the different types of Big Data that can be drawn from various sources like media files, recordings, log files, images, etc.
  
  

• Volume - It describes the number of users, tables, data sizes, and records in datasets. 
  
  

• Veracity - It represents the uncertainty or certainty that comes with the data in terms of its accuracy. 

Q4. How can you differentiate between structured and unstructured data?

Several pointers can help discern between structured and unstructured data. 

Structured Data	Unstructured Data
Stored in traditional database architecture like MS Access, SQL Server, and Oracle in rows and columns.	It can't be stored using any such methods and is mostly unmanaged.
It is defined according to the data model.	It can't be defined in terms of data model since it has a varying size and content.

Q5. Define Hadoop and its components in brief.

1. Hadoop is an open-source platform that is widely used to process data and store these in smaller units called clusters. It is considered imperative to compile massive datasets simultaneously to give secure provisions for storing data. The different components are:
   
   

• Hadoop Common - All libraries and utilities used in the Hadoop app.
  
  
• HDFS - All the Hadoop data is stored and gives access to a high-bandwidth, distributed file system. 
  
  
• Hadoop YARN - A resource negotiator in managing resources in the Hadoop system, and schedules tasks. 
  
  
• Hadoop MapReduce - It provides access to users for large-scale data processing. 
  
  

Q6. What is the hardest part of being a data engineer?

Make sure, to be honest, and transparent in your answer. 

For a data engineer, it can get overwhelming to keep up with the requirements of all departments in an organization. Sometimes, one's demands can conflict with the others, and it is the scope of a data engineer to find the right balance, in the given company infrastructure.

Q7. What do you know about the Reducer stage in Hadoop MapReduce?

1. The second phase of data processing in Hadoop Framework is known as Reducer, where it analyzes the yield data from the mapper and generates a refined output that gets stored in HDFS. In general, the Reducer stage has three phases, including shuffle, sorting, and reduce. 
   
   

Q8. Define block and block scanner in HDFS.

Block can be defined as the singularity of data in its simplest form. In Hadoop, as the system comes in contact with a large file, it automatically breaks down the records into smaller units, known as blocks.

The block scanner helps in screening the loss-of-blocks is effectively deployed or not. 

Q9. Which ETL tools are you most comfortable with?

1. You can list out all the tools you have worked with (including your preferences). 
   
   

I have worked extensively with SAS Data Management, IBM Infosphere, and SAP Data Services. However, I like PowerCenter the most. Its high-efficiency, high-end optimization, and performance abilities make it a handy tool for me.

Q10. What are the different schemas of data modeling?

1. There are two types of data modeling schemas:
   
   

Star Schema - It includes two interconnected tables, including fact table and dimension table. It is mostly famous for its simple data mart style. 

Snowflake Schema - It is an extension to star schema and adds dimension. 

While data engineering may seem more like a predefined routine job, but for passionate engineers curious about epic data insights, it can be a great career option. However, real-time data engineering requires more than definitions and comes with advanced data engineer courses that help you understand the scenario-based application. Only then you can spread your wings further and perform better in an organization.